Along with the introduction of driver assistance systems and fully or partially self-driving cars, it is becoming increasingly important to accurately determine the location of the vehicle.
Localization of a self-driving vehicle is for example proposed to be solved using multiple redundant technologies, of which local sensing (such as radar, lidar and cameras) in combination with a map of recognizable landmarks is one major component.
However, a problem when performing localization using camera images is to create a map which is invariant to the visual changes that occur over time in the environment surrounding the vehicle. Changes in lighting during the day, changing weather, seasonal variation, etc. may cause significant changes in the appearance of the same scene, making it difficult to extract features from the images that remain constant.
Current solutions are able to extract image features (e.g., SIFT, SURF, ORB), build a map based on the image features and to localize the vehicle in this map shortly after. To have a map that works at all times some experiments have been made to continuously add image features as they change appearance over time and thus keeping the map updated. However, this approach has drawbacks because of an ever-growing map, and that each area must be revisited relatively soon so that not too many of the last set of features have changed to be able to localize.
Another approach includes place recognition where one does not localize with the same precision, using triangulation of landmarks, but where the objective is to get a rough idea of where the image was taken. This approach has proven more robust against temporal changes, but has too low precision to be useful for self-driving cars.
Accordingly, there is room for improvement relating to the localization of vehicles using camera images.